Recent displacement trends show growing numbers of displaced population living outside of designated areas such as camp/camp like setting (traditional camps collective/transit/reception centres, informal settlements) with a majority setting in dispersed locations predominately urban and peri-urban areas such as informal settlements, unfinished buildings or interspersed in host community homes and communities, shared rooms or rental arrangements. To be able to reach, properly assess and understand local dynamics, vulnerabilities and capacities of the displaced and host populations alike, humanitarian organisations are increasingly using sub-national Area Based Approach. Area based approaches define “an area, rather than a sector or target group, as a primary entry point”. Such approach is particularly appropriate when residents in an affected area face complex, inter-related and multisectoral needs, resulting in risk of forced displacement.

Area Based Approach for Forced Displacement: Characterisation through Sensor Data

In the context of migration statistics, forced displacement is often analyzed with the prism of push and pull factors.

Push Factor (Mitigated by intervention to address root causes within countries of origin) Pull Factor (Mitigated by migration & Asylum policies of receiving countries)
Economic Dimension (Addressed by programme in relation with development & poverty alleviation) Lack of public services, Unemployement, Overpopulation More jobs, Better jobs, Higher wages, promise of a “better individual life”
Social Dimension (Addressed by programme in relation with protection) Violence, insecurity, intolerance towards certain groups, active political or religious persecution, Safety, tolerance, freedom
Environmental Dimension (Addressed by programme in relation with resilience & sustainability) Climate change, natural disasters More livable environment

Though, traditional statistical data sources are often lacking sufficient geographically-fine-grained disaggregation to inform sub national scale approach and characterization. Alternative based on sophisticated index like Inform Colombia requires extensive expert consultations and might not fully reflect the important dimension to be reflected in the context of forced displacement and migration.

Proposed methodology

New sensors provide unique abilities to capture new flow of information from social medias (Anonymized data from Facebook platform) at subnational scale through grid level information. Satellite data can pick up signals of economic activity by detecting light at night, it can pick up development status by detecting infrastructure such as roads, and it can pick up signals for individual household wealth by detecting different building footprints and roof types.

In regard to the framework above, an initial selection of globally available layers includes:

  • Economics:
    • Weighted Relative Wealth Index (Facebook-RWI)
    • Social Connectedness Index (Facebook-SCI)
    • Public Services Catchment area (OSM)
  • Environment:
    • Agricultural drought frequency Index (FAO-ASI)
    • Climatic Natural Risk - Flood and Cyclone (Prevention Web)
    • Geologic Natural Risk - Earthquake and Volcano (Prevention Web)
  • Social:
    • Population Dependency Ratio (Facebook)
    • Movement Range data sets Facebook
    • Violence (ACLED)

Information can be compiled and aggregated at admin level 2 in order to build composite Indicators. Different areas can be then grouped together based on the values from those composite indicators. The advantage of this approach are multiple: 1. Granularity: Optimal Level of granularity 2. Availibility: Data Consistently and freely available worldwide, simplicity to obtain information, ensor based indicators are potentially less sensitive to political pressure 3. Reproducibility: Can be used in multiple countries easily and Fully automated and audited through reproducible analysis script

The resulting information can complement other traditional source of information both on quantitative (Household Survey) and qualitative (Focus Group Discussions) side.

Social:

Population Dependency Ratio (Facebook)

Population movement range

The Movement Range data sets is intended to inform on how populations are responding to physical distancing measures. In particular, there are two metrics that provide a slightly different perspective on movement trends:

  • Change in Movement: looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures. The idea is to understand how much less people are moving around since the onset of the coronavirus epidemic. This is done by quantifying how much people move around by counting the number of level-16 Bing tiles (which are approximately 600 meters by 600 meters in area at the equator) they are seen in within a day. In the dataset noted all_day_bing_tiles_visited_relative_change

  • Stay Put: looks at the fraction of the population that appear to stay within a small area during an entire day. This metric intends to measure this by calculating the percentage of eligible people who are only observed in a single level-16 Bing tile during the course of a day. In the dataset noted all_day_ratio_single_tile_users

Violence

Economics

Relative Wealth Index

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty.

As explained in a dedicated paper, the Relative Wealth Index estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook.

As described in the this tutorial

  • Determine which administrative unit contains the centroid of each RWI tile

  • Calculate the bing tile quadkey at zoom level 14 for each point in the population density dataset and sum the population per level 14 tile

  • Determine which zoom level 14 (~2.4km bing tile) corresponds to each of the smaller 30m population density tiles, and calculate the sum of population within each zoom level 14 tile.

  • Calculate the total population in each administrative region using the population density dataset

  • Calculate a population derived weight for each zoom level 14 RWI tile

  • Use the weight value to calculate a weighted RWI value and aggregate to the administrative unit level

Social Connectedness Index

an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook.

http://pages.stern.nyu.edu/~jstroebe/PDF/BGHKRS_InternationalTradeSocialConnectedness_JIE.pdf

https://data.humdata.org/dataset/social-connectedness-index

Public Services

Environment:

Agricultural drought frequency Index

FAO-ASI)

Historic Agricultural Drought Frequency Maps depict the frequency of severe drought in areas where 30 percent/50 percent of the cropland has been affected. The historical frequency of severe droughts (as defined by ASI) is based on the entire ASI times series (1984-2020).

Climatic Natural Risk - Flood and Cyclone (Prevention Web)

Geologic Natural Risk - Earthquake and Volcano (Prevention Web)

Clustering analysis of different areas

Composite indicators

The polarity of a sub-indicator is the sign of the relationship between the indicator and the phenomenon to be measured (e.g., in a well-being index, “GDP per capita” has ‘positive’ polarity and “Unemployment rate” has ‘negative’ polarity). In this case, we have 2 options for such directional adjustments:

  • “Negative (the higher score, the more severe)”
  • “Positive (the higher score, the less severe)”

This component is accounted for during the normalization process below.

Data Normalization allows for Adjustments of distribution (similar range of variation) and scale (common scale) of sub-indicators that may reflect different units of measurement and different ranges of variation.

Due to the structure of the indicators, distinct approaches of normalization shall be considered in order to avoid having zero value that would create issues for geometric means aggregation. Different normalization methods are available through the function normalise_ci and lead to different results:

  • A z-score approach method = 1 (Imposes a distribution with mean zero and variance 1). Standardized scores which are below average will become negative, implying that further geometric aggregation will be prevented.

  • A min-max approach method = 2 (same range of variation [0,1]) but not same variance).This method is very sensitive to extreme values / outliers

  • A ranking method method = 3. Scores are replaced by ranks – e.g. the highest score receives the first ranking position (rank 1).

Data Normalization allows for Adjustments of distribution (similar range of variation) and scale (common scale) of sub-indicators that may reflect different units of measurement and different ranges of variation.

Due to the structure of the indicators, distinct approaches of normalization shall be considered in order to avoid having zero value that would create issues for geometric means aggregation. Different normalization methods are available through the function normalise_ci and lead to different results:

  • A z-score approach method = 1 (Imposes a distribution with mean zero and variance 1). Standardized scores which are below average will become negative, implying that further geometric aggregation will be prevented.

  • A min-max approach method = 2 (same range of variation [0,1]) but not same variance).This method is very sensitive to extreme values/outliers

  • A ranking method method = 3. Scores are replaced by ranks – e.g. the highest score receives the first ranking position (rank 1).

Correlation analysis

The investigation of the structure of simple indicators can be done by means of correlation analysis.

We will check such correlation first within each dimension, using the ggcorrplot package. An alternative approach to better visualize correlation between indicators is to represent them through a network with the ggpraph package.

#> From     To  Weight
#> 1     ---     2   -0.45 
#> 1     ---     3   -0.27 
#> 2     ---     3   -0.32 
#> 1     ---     4   -0.39 
#> 2     ---     4   0.21 
#> 3     ---     4   -0.16 
#> 1     ---     5   -0.23 
#> 2     ---     5   -0.09 
#> 3     ---     5   0.47 
#> 4     ---     5   -0.12 
#> 1     ---     6   -0.02 
#> 2     ---     6   -0.16 
#> 3     ---     6   0.25 
#> 4     ---     6   -0.07 
#> 5     ---     6   0.4 
#> 1     ---     7   0.06 
#> 2     ---     7   -0.16 
#> 3     ---     7   0.2 
#> 4     ---     7   -0.11 
#> 5     ---     7   0.48 
#> 6     ---     7   0.4 
#> 1     ---     8   0.01 
#> 2     ---     8   -0.19 
#> 3     ---     8   0.33 
#> 4     ---     8   -0.14 
#> 5     ---     8   0.61 
#> 6     ---     8   0.48 
#> 7     ---     8   0.8 
#> 1     ---     9   -0.12 
#> 2     ---     9   -0.14 
#> 3     ---     9   0.46 
#> 4     ---     9   -0.17 
#> 5     ---     9   0.85 
#> 6     ---     9   0.46 
#> 7     ---     9   0.73 
#> 8     ---     9   0.76

#> From     To  Weight
#> 1     ---     2   0.74 
#> 1     ---     3   1 
#> 2     ---     3   0.74 
#> 1     ---     4   0.83 
#> 2     ---     4   0.68 
#> 3     ---     4   0.82 
#> 1     ---     5   -0.45 
#> 2     ---     5   -0.39 
#> 3     ---     5   -0.46 
#> 4     ---     5   -0.28 
#> 1     ---     6   -0.12 
#> 2     ---     6   -0.22 
#> 3     ---     6   -0.12 
#> 4     ---     6   -0.12 
#> 5     ---     6   0.04 
#> 1     ---     7   -0.39 
#> 2     ---     7   -0.45 
#> 3     ---     7   -0.39 
#> 4     ---     7   -0.31 
#> 5     ---     7   0.63 
#> 6     ---     7   0.57 
#> 1     ---     8   -0.34 
#> 2     ---     8   -0.2 
#> 3     ---     8   -0.34 
#> 4     ---     8   -0.17 
#> 5     ---     8   0.85 
#> 6     ---     8   -0.39 
#> 7     ---     8   0.24

Consistency between indicators

Cronbach’s alpha, (or coefficient alpha), developed by Lee Cronbach in 1951, measures reliability (i.e. how well a test measures what it should: measure of the stability of test scores), or internal consistency.

As a rule of thumbs, a score of more than 0.7 indicates an acceptable level of consistency:

  • A high level for alpha may mean that all indicators are highly correlated (meaning we have redundant indicators representing the same thing…).
  • A low value for alpha may mean that there are not enough indicators or that the indicators are poorly interrelated.

The Cronbach Alpha measure of consistency for this combination of indicators is  0.85
.

Aggregation & Weighting

For weighting, the main issue to address is related to the concept of compensability. Namely the question is to know to what extent can we accept that the high score of an indicator go to compensate the low score of another indicator? This problem of compensability is intertwined with the issue of attribution of weights for each sub-indicator in order to calculate the final aggregation.

We can foresee that using “equal weight” (all indicators account for the same in the final index) and “arithmetic aggregation” (all indicators are substituable) is unlikely to depict the complex issue of Humanitarian Severity and is likely to comes with the risk of misrepresenting the reality.

Various methods are available within the Compind package are described below. This R package is also available through a ShinyApp. We will then share the code to use them based on our example.

Benefit of the Doubt approach (BoD)

This method is the application of Data Envelopment Analysis (DEA) to the field of composite indicators. It was originally proposed by Melyn and Moesen (1991) to evaluate macroeconomic performance. ACAPS has prepared an excellent note on The use of data envelopment analysis to calculate priority scores in needs assessments.

BoD approach offers several advantages:

  • Weights are endogenously determined by the observed performances and benchmark is not based on theoretical bounds, but it’s a linear combination of the observed best performances.

  • Principle is easy to communicate: since we are not sure about the right weights, we look for ”benefit of the doubt” weights (such that your overall relative performance index is as high as possible).

Directional Benefit of the Doubt (D-BoD)

Directional Benefit of the Doubt (D-BoD) model enhances non-compensatory property by introducing directional penalties in a standard BoD model in order to consider the preference structure among simple indicators. This method is described in the article Enhancing non compensatory composite indicators: a directional proposal.

Robust Benefit of the Doubt approach (RBoD)

This method is the robust version of the BoD method. It is based on the concept of the expected minimum input function of order-m so “in place of looking for the lower boundary of the support of F, as was typically the case for the full-frontier (DEA or FDH), the order-m efficiency score can be viewed as the expectation of the maximal score, when compared to m units randomly drawn from the population of units presenting a greater level of simple indicators”, Daraio and Simar (2005). This method is described with more detail in the article Robust weighted composite indicators by means of frontier methods with an application to European infrastructure endowment.

Factor analysis

This method groups together simple indicators to estimate a composite indicator that captures as much as possible of the information common to individual indicators.

Mean-Min Function (MMF)

This method is an intermediate case between arithmetic mean, according to which no unbalance is penalized, and min function, according to which the penalization is maximum. It depends on two parameters that are respectively related to the intensity of penalization of unbalance (alpha) and intensity of complementarity (beta) among indicators. “An unbalance adjustment method for development indicators”

Geometric aggregation

This method uses the geometric mean to aggregate the single indicators and therefore allows to bypass the full compensability hypothesis using geometric mean. Two weighting criteria are possible: EQUAL: equal weighting and BOD: Benefit-of-the-Doubt weights following the Puyenbroeck and Rogge (2017) approach.

Mazziotta-Pareto Index (MPI)

This method is is a non-linear composite index method which transforms a set of individual indicators in standardized variables and summarizes them using an arithmetic mean adjusted by a “penalty” coefficient related to the variability of each unit (method of the coefficient of variation penalty).

Wroclaw taxonomy method

This last method (also known as the dendric method), originally developed at the University of Wroclaw, is based on the distance from a theoretical unit characterized by the best performance for all indicators considered; the composite indicator is therefore based on the sum of euclidean distances from the ideal unit and normalized by a measure of variability of these distance (mean + 2*std).

Visualise output

In a table

Composite with different algorithm
Benef_Doubt Benef_Doubt_Rob Benef_Doubt_Dir Factor Mean_Geom Mean_Min Mazziotta_Pareto Wroclaw
SV0101 0.9194611 1.5242046 0.8949529 -0.0947139 NaN 90661.70 844464.7 0.4947580
SV0102 0.7213726 1.2400413 0.6808292 0.0437439 NaN 53286.75 927216.5 0.4947580
SV0103 0.8317471 1.5207861 0.7969113 0.2336051 NaN 6901017.19 124194446.7 0.4947580
SV0104 0.7894281 1.3239778 0.7313262 0.1263475 NaN 17129831.00 308324843.5 0.4947580
SV0105 1.0000000 2.1030404 1.0000000 1.3085004 NaN 6173700.21 111096123.5 0.4947580
SV0106 0.6864198 1.0260029 0.6680960 -0.1581458 NaN 5768318.54 103785007.2 0.4947580
SV0107 0.6864198 1.0661544 0.6351077 -0.3743122 NaN 97691.59 930895.2 0.4947580
SV0108 0.7281155 1.2490068 0.7038459 -0.3360233 NaN 156000.77 2792156.3 0.4947580
SV0109 0.6864198 1.2462063 0.6435774 -0.1127151 NaN 90660.21 844479.4 0.4947580
SV0110 0.6864198 0.9316551 0.7033525 -0.0827130 NaN 53286.04 927229.3 0.4947580
SV0111 0.6894908 1.2413580 0.6501203 -0.4249942 NaN 6901016.30 124194462.9 0.4947580
SV0112 0.6864198 NaN 0.6605382 -0.3957867 0.00000 17129823.07 308324911.6 0.4947580
SV0201 0.9818478 1.5723288 0.7834489 0.2962558 NaN 12052786.93 216884544.6 0.1016491
SV0209 0.9818478 1.4432037 0.7954109 0.1339835 NaN 11260509.60 202627140.1 0.1016491
SV0202 1.0000000 2.0398984 1.0000000 1.2046493 NaN 286092.74 3246994.2 0.1016491
SV0203 1.0000000 1.7788452 1.0000000 0.2271373 NaN 541778.01 9730169.6 0.1016491
SV0204 0.9818478 -0.6996497 0.7097491 0.0850992 NaN 262393.74 2886842.4 0.1016491
SV0205 0.9818478 1.3661823 0.7878894 0.3141720 NaN 183625.62 3254450.9 0.1016491
SV0206 0.9980557 2.0182485 0.9911112 -0.0598407 NaN 13472858.05 242452769.2 0.1016491
SV0207 0.9818478 1.3145737 0.7991882 0.2837598 NaN 33441626.06 601931262.1 0.1016491
SV0208 0.9818478 1.4440589 0.7413614 0.0608902 NaN 12052787.01 216884543.1 0.1016491
SV0301 1.0000000 1.7432539 1.0000000 0.2304805 NaN 11393148.91 205012755.3 0.0912800
SV0302 1.0000000 -10.2277352 1.0000000 0.2957699 NaN 508448.61 6970977.3 0.0912800
SV0303 1.0000000 1.3842487 1.0000000 0.5359961 NaN 1091771.20 19629116.0 0.0912800
SV0307 1.0000000 2.2839001 1.0000000 0.7622844 NaN 461390.75 6177244.1 0.0912800
SV0304 1.0000000 1.4972162 1.0000000 0.3695036 NaN 368837.87 6586149.4 0.0912800
SV0305 1.0000000 2.5213984 1.0000000 0.9731282 NaN 13632631.32 245287463.4 0.0912800
SV0306 1.0000000 1.6139854 1.0000000 0.8792913 NaN 33835468.62 609019732.7 0.0912800
SV0308 1.0000000 1.4142465 1.0000000 0.5576782 NaN 12195827.57 219418299.7 0.0912800
SV0309 1.0000000 1.6537204 1.0000000 0.7264762 NaN 11393159.67 205012729.3 0.0912800
SV0310 1.0000000 2.5220636 1.0000000 1.0217532 NaN 508450.37 6970952.4 0.0912800
SV0399 1.0000000 2.3045169 1.0000000 0.5178707 NaN 1091766.50 19629120.4 0.0912800
SV0311 1.0000000 1.2292026 1.0000000 0.6033997 NaN 461390.00 6177254.4 0.0912800
SV0312 1.0000000 1.6576023 1.0000000 0.4986173 NaN 368839.39 6586121.5 0.0912800
SV0313 1.0000000 2.0021155 1.0000000 0.2535241 NaN 13632631.50 245287459.4 0.0912800
SV0314 1.0000000 1.4281549 1.0000000 0.3997261 NaN 33835466.62 609019769.8 0.0912800
SV0315 1.0000000 1.6335552 1.0000000 0.4907681 NaN 12195827.39 219418303.1 0.0912800
SV0316 1.0000000 2.0211925 1.0000000 0.1758513 NaN 11393155.38 205012739.9 0.0912800
SV0317 1.0000000 -9.0815358 1.0000000 0.3093379 NaN 508449.19 6970968.9 0.0912800
SV0318 1.0000000 1.8851446 1.0000000 0.4706329 NaN 1091757.64 19629145.6 0.0912800
SV0319 1.0000000 2.0288730 1.0000000 0.8214725 NaN 461391.04 6177240.1 0.0912800
SV0320 1.0000000 1.4017035 1.0000000 0.7179746 NaN 368839.33 6586122.5 0.0912800
SV0321 1.0000000 7.0530124 1.0000000 0.9429774 67.64960 13632630.89 245287471.8 0.0912800
SV0322 1.0000000 1.5857098 1.0000000 0.3936039 NaN 33835466.91 609019764.4 0.0912800
SV0323 1.0000000 NaN 1.0000000 0.6438580 0.00000 12195827.76 219418296.0 0.0912800
SV0324 1.0000000 1.7251497 1.0000000 0.2190583 NaN 11393151.08 205012750.7 0.0912800
SV0325 1.0000000 2.1861594 1.0000000 1.2509124 NaN 508450.12 6970956.0 0.0912800
SV0326 1.0000000 -17.0510933 1.0000000 0.3333679 NaN 1091774.81 19629109.4 0.0912800
SV0327 1.0000000 1.6840523 1.0000000 0.8968648 NaN 461390.64 6177245.5 0.0912800
SV0328 1.0000000 1.6252072 1.0000000 0.8258141 NaN 368839.45 6586120.3 0.0912800
SV0329 1.0000000 NaN 1.0000000 0.8509805 0.00000 13632630.64 245287477.1 0.0912800
SV0330 1.0000000 1.3609000 1.0000000 0.6479529 NaN 33835467.29 609019757.3 0.0912800
SV0331 1.0000000 1.9745691 1.0000000 0.8406961 NaN 12195828.18 219418288.6 0.0912800
SV0332 1.0000000 2.2907136 1.0000000 0.8405965 NaN 11393169.17 205012709.6 0.0912800
SV0333 1.0000000 1.6824563 1.0000000 0.4109148 NaN 508448.87 6970973.7 0.0912800
SV0401 0.7363430 2.4040368 0.8063895 -0.0896493 60.36462 190191.06 3409409.0 0.4193858
SV0402 0.9384300 3.1104419 0.9291990 0.2470026 67.82130 109151.00 1027068.4 0.4193858
SV0403 0.7207402 2.3384000 0.7928957 -0.2480113 44.91077 64594.07 1138355.8 0.4193858
SV0404 0.6882506 2.5185922 0.7804299 -0.1631927 44.38233 8160548.53 146866472.9 0.4193858
SV0405 0.5865221 1.0474520 0.6993295 -0.4024191 NaN 20256457.80 364606924.5 0.4193858
SV0406 0.5865221 0.9511994 0.7115131 -0.4104881 NaN 7300431.40 131378020.1 0.4193858
SV0407 1.0000000 NaN 1.0000000 0.2982969 0.00000 6820796.06 122737185.4 0.4193858
SV0408 0.5865221 0.9301553 0.6881748 -0.4872122 NaN 117705.49 1134006.1 0.4193858
SV0409 0.6255702 1.1048449 0.6727310 -0.5944882 NaN 190158.65 3409502.4 0.4193858
SV0410 0.6523534 1.2894034 0.6910279 -0.4693073 NaN 109149.09 1027087.0 0.4193858
SV0411 0.7197160 2.3652624 0.7951742 -0.1968118 55.58076 64593.90 1138358.9 0.4193858
SV0412 0.7734272 1.3499600 0.8217365 0.1755861 NaN 8160548.99 146866464.0 0.4193858
SV0413 0.6376931 1.1710264 0.7523449 -0.2369856 NaN 20256458.05 364606919.8 0.4193858
SV0414 0.8193156 2.7977535 0.8474973 0.0531996 66.81184 7300432.39 131378002.7 0.4193858
SV0415 0.6856693 1.3935058 0.7039253 -0.9587641 NaN 6820779.66 122737288.6 0.4193858
SV0416 0.5865221 1.0998363 0.6276394 -0.8198250 NaN 117704.74 1134013.6 0.4193858
SV0501 0.9529370 2.3387490 0.9031420 0.5114316 62.53936 77433.70 1380282.1 0.7887146
SV0502 0.6172686 1.1184773 0.6661029 -0.4765015 NaN 37910.79 408355.6 0.7887146
SV0503 0.7629616 1.5303225 0.7145333 -0.1451315 NaN 26574.24 456479.3 0.7887146
SV0504 0.8140028 1.9606642 0.7792984 0.0070913 NaN 1989391.30 35794353.1 0.7887146
SV0505 0.5453983 1.4096827 0.6142868 -1.0215120 NaN 4937701.38 88869715.4 0.7887146
SV0506 0.6211120 0.9885191 0.6573441 -0.4700114 NaN 1779864.93 32016677.4 0.7887146
SV0507 0.6284139 1.0806481 0.6755037 -0.3179317 NaN 1663306.95 29904117.5 0.7887146
SV0508 0.5969643 1.1227694 0.6442096 -0.6736342 NaN 41385.65 458081.1 0.7887146
SV0509 0.7595268 1.2112871 0.7340787 -0.4183464 NaN 77400.04 1380403.4 0.7887146
SV0510 1.0000000 1.6624608 1.0000000 0.0435492 NaN 37912.42 408337.5 0.7887146
SV0511 0.7198485 NaN 0.7508032 -0.1133161 0.00000 26573.36 456494.9 0.7887146
SV0512 0.8162090 1.3058060 0.7842189 -0.3513348 NaN 1989391.33 35794352.2 0.7887146
SV0513 0.8160783 1.2785780 0.7826743 -0.3827027 NaN 4937703.39 88869678.0 0.7887146
SV0514 0.7678343 1.7335222 0.7518938 0.1739094 NaN 1779866.12 32016655.5 0.7887146
SV0515 0.5481020 0.8303749 0.6414871 -0.4388751 NaN 1663305.90 29904123.0 0.7887146
SV0516 0.5695234 0.9528646 0.6312332 -0.5973940 NaN 41385.89 458078.3 0.7887146
SV0517 0.6755459 1.0882556 0.6719388 -0.5780187 NaN 77402.93 1380399.7 0.7887146
SV0518 0.5358025 0.9935136 0.6049538 -0.7788144 NaN 37909.89 408365.7 0.7887146
SV0519 0.6203990 1.1645161 0.6625409 -0.5969692 NaN 26571.48 456528.3 0.7887146
SV0520 0.5899952 1.2698285 0.6453706 -0.7191090 NaN 1989390.05 35794375.9 0.7887146
SV0521 0.5492905 0.9692710 0.6344361 -0.5790601 NaN 4937701.30 88869716.7 0.7887146
SV0522 0.7597752 1.6983239 0.7474815 0.1682765 51.94887 1779866.04 32016657.2 0.7887146
SV0601 0.7916144 2.2618488 0.8190611 0.2480160 61.33395 4296612.59 77301653.2 0.6001616
SV0602 0.6902223 1.6361296 0.7119886 -0.1338610 NaN 60748.93 542858.2 0.6001616
SV0603 0.5654321 NaN 0.6203429 -0.7049447 NaN 86315.41 1539736.1 0.6001616
SV0604 0.5654321 NaN 0.6350955 -0.6328661 0.00000 56742.98 502391.2 0.6001616
SV0605 0.7268183 1.3550014 0.7174943 -0.1110973 NaN 29609.43 509370.8 0.6001616
SV0606 0.6337676 0.9188767 0.6998413 -0.1001332 NaN 5140125.93 92506531.2 0.6001616
SV0607 0.5654321 0.7364278 0.5954317 -0.8036686 NaN 12759052.29 229653676.8 0.6001616
SV0608 0.5654321 0.6375105 0.6338615 -0.4960791 NaN 4598432.32 82749515.3 0.6001616
SV0609 0.5654321 0.7019601 0.6215832 -0.6087410 NaN 4296600.05 77301722.5 0.6001616
SV0610 0.6208852 1.0249234 0.6521080 -0.4897613 NaN 60748.45 542862.7 0.6001616
SV0611 0.7409518 2.1654213 0.7757908 -0.0236248 51.99007 86323.39 1539668.1 0.6001616
SV0612 0.6135406 1.1103117 0.6544456 -0.4908149 NaN 56742.54 502395.2 0.6001616
SV0613 0.5715944 0.9323178 0.6424511 -0.4720511 NaN 29607.13 509411.6 0.6001616
SV0614 0.7394410 1.3358801 0.7305990 -0.3360184 NaN 5140127.04 92506510.2 0.6001616
SV0615 0.5882616 0.9174340 0.6775691 -0.1936678 NaN 12759054.16 229653644.1 0.6001616
SV0616 0.6339448 1.1807947 0.6610592 -0.3309438 NaN 4598433.15 82749500.7 0.6001616
SV0617 0.6066436 0.9380809 0.6718918 -0.2773434 NaN 4296607.04 77301703.6 0.6001616
SV0618 0.7718140 1.6203013 0.7727030 0.3178601 NaN 60750.14 542846.7 0.6001616
SV0619 0.6109715 1.1849127 0.6729983 -0.1910650 NaN 86308.31 1539744.0 0.6001616
SV0620 0.6957287 1.2264288 0.6956683 -0.5067066 NaN 56743.35 502387.7 0.6001616
SV0621 0.5821554 0.9515492 0.6415286 -0.4943263 NaN 29605.87 509434.1 0.6001616
SV0622 0.8428911 1.4158023 0.8166558 -0.6480729 NaN 5140127.41 92506503.2 0.6001616
SV0701 0.9135802 1.2502103 0.7641091 0.2764584 NaN 22227123.25 400071500.0 0.3715732
SV0702 0.9135802 1.1820301 0.7229748 0.1982383 NaN 8011358.84 144143873.2 0.3715732
SV0703 0.9135802 1.1827920 0.6960414 0.0943616 NaN 7484783.76 134667468.8 0.3715732
SV0704 0.9135802 1.4895872 0.8394947 0.2409350 NaN 243225.55 3022968.1 0.3715732
SV0705 0.9135802 1.3651643 0.8042873 0.2650405 NaN 491543.14 8827230.1 0.3715732
SV0706 0.9135802 1.1548116 0.7166341 0.1210013 NaN 221865.27 2679626.6 0.3715732
SV0707 0.9135802 1.1437153 0.7998770 0.4344638 NaN 166600.18 2952553.5 0.3715732
SV0708 0.9135802 1.7987922 0.8220317 0.4696630 NaN 8955144.11 161139023.0 0.3715732
SV0709 0.9135802 1.0381316 0.7011597 0.1220597 NaN 22227122.15 400071520.5 0.3715732
SV0710 0.9857579 2.0442338 0.9780034 1.0337916 NaN 8011360.59 144143841.9 0.3715732
SV0711 0.9135802 1.1325333 0.7427393 0.1647848 NaN 7484786.78 134667462.3 0.3715732
SV0712 0.9135802 1.3338723 0.7736856 0.1423549 NaN 243225.40 3022969.9 0.3715732
SV0713 0.9135802 1.3778836 0.7329385 0.0925239 NaN 491552.77 8827215.0 0.3715732
SV0715 0.9135802 1.3018435 0.8004699 0.4089868 NaN 221865.39 2679625.2 0.3715732
SV0716 0.9135802 1.6236206 0.8324595 0.4867143 NaN 166601.75 2952524.8 0.3715732
SV0717 0.9135802 1.1536286 0.7642985 0.3818127 NaN 8955143.28 161139038.3 0.3715732
SV0718 0.9135802 0.9934119 0.7295670 0.2330178 NaN 22227121.87 400071525.8 0.3715732
SV0801 1.0000000 1.1170882 1.0000000 -0.1963914 NaN 13557627.72 243999178.4 0.0206319
SV0802 1.0000000 1.2522006 1.0000000 -0.3737105 NaN 12666778.92 227952872.8 0.0206319
SV0803 1.0000000 1.2045589 1.0000000 -0.2544677 NaN 171376.19 1528157.0 0.0206319
SV0804 1.0000000 1.4152617 1.0000000 -0.0598076 NaN 236304.49 4235752.1 0.0206319
SV0805 1.0000000 1.2693748 1.0000000 -0.2056170 NaN 160558.65 1419891.3 0.0206319
SV0806 1.0000000 1.3594204 1.0000000 -0.0768428 NaN 80485.91 1410117.5 0.0206319
SV0807 1.0000000 -0.7396564 1.0000000 -0.3355782 NaN 15155150.60 272761134.1 0.0206319
SV0808 1.0000000 1.1638558 1.0000000 -0.1523468 NaN 37619489.07 677136999.3 0.0206319
SV0809 1.0000000 1.2595183 1.0000000 -0.3395796 NaN 13557627.62 243999180.6 0.0206319
SV0810 1.0000000 1.4690624 1.0000000 -0.2257705 NaN 12666775.16 227952876.3 0.0206319
SV0811 1.0000000 1.2865198 1.0000000 -0.2428388 NaN 171375.58 1528162.9 0.0206319
SV0812 1.0000000 1.6906028 1.0000000 -0.0219774 NaN 236305.57 4235748.3 0.0206319
SV0813 1.0000000 1.3381579 1.0000000 -0.1199302 NaN 160558.73 1419890.5 0.0206319
SV0814 1.0000000 1.1832419 1.0000000 -0.2496585 NaN 80484.55 1410142.1 0.0206319
SV0815 1.0000000 2.1408562 1.0000000 0.2235325 NaN 15155152.49 272761098.6 0.0206319
SV0816 1.0000000 -3.4016370 1.0000000 -0.4070202 NaN 37619488.39 677137011.9 0.0206319
SV0817 1.0000000 1.2096804 1.0000000 -0.1433825 NaN 13557627.81 243999176.8 0.0206319
SV0818 1.0000000 NaN 1.0000000 -0.3249928 0.00000 12666786.38 227952863.3 0.0206319
SV0819 1.0000000 1.6195324 1.0000000 0.0234642 NaN 171376.83 1528151.0 0.0206319
SV0820 1.0000000 2.5516592 1.0000000 0.0785246 49.02393 236325.48 4235704.4 0.0206319
SV0821 1.0000000 1.3278579 1.0000000 -0.1138065 NaN 160559.41 1419884.3 0.0206319
SV0822 1.0000000 1.1367817 1.0000000 -0.1886251 NaN 80482.81 1410163.2 0.0206319
SV0823 1.0000000 1.7700412 1.0000000 -0.2331888 NaN 15155152.21 272761103.9 0.0206319
SV0824 1.0000000 12.0977330 1.0000000 -0.4363100 NaN 37619486.40 677137030.2 0.0206319
SV0825 1.0000000 1.3535501 1.0000000 -0.4753351 NaN 13557627.45 243999183.6 0.0206319
SV0826 1.0000000 1.1767180 1.0000000 0.1652175 NaN 12666784.03 227952857.8 0.0206319
SV0901 0.8197531 0.9921982 0.7318074 0.2529125 NaN 140089.29 1743284.7 0.6031695
SV0902 0.8197531 1.0076075 0.6955033 0.0842369 NaN 283665.81 5087175.4 0.6031695
SV0903 0.8197531 1.1041848 0.7146888 0.1419815 NaN 127740.02 1545402.2 0.6031695
SV0904 0.8197531 0.9879240 0.7049323 0.1158635 NaN 96469.76 1696064.4 0.6031695
SV0905 0.8200063 1.3497935 0.7700529 -0.0095502 NaN 5087534.98 91539213.6 0.6031695
SV0906 0.8197531 1.0546946 0.7074183 0.1685485 NaN 12627252.39 227275562.5 0.6031695
SV0907 0.8772041 1.4613625 0.8238734 0.4529798 NaN 4551449.69 81883001.8 0.6031695
SV0908 0.8197531 1.0086038 0.7047868 0.1418203 NaN 4252607.14 76494026.0 0.6031695
SV0909 0.8197531 1.1700389 0.7411889 0.2964299 NaN 140089.89 1743276.8 0.6031695
SV0910 1.0000000 2.9555485 1.0000000 1.0906217 86.22503 283696.63 5087061.8 0.6031695
SV0911 0.8197531 1.0712359 0.6565612 -0.0809925 NaN 127738.95 1545415.6 0.6031695
SV0912 0.9257197 1.6916297 0.8991947 0.8606907 NaN 96472.35 1696017.3 0.6031695
SV0913 0.8197531 -0.8643748 0.6896325 0.0058682 NaN 5087532.92 91539252.2 0.6031695
SV0914 0.8197531 1.2796457 0.6554429 -0.1778691 NaN 12627252.26 227275564.9 0.6031695
SV0915 0.9078385 1.3481680 0.8781513 0.5934302 NaN 4551449.70 81883001.8 0.6031695
SV0916 0.8197531 1.1864942 0.7064260 0.0588150 NaN 4252604.43 76494031.3 0.6031695
SV0917 1.0000000 1.7651749 1.0000000 0.0742432 NaN 140088.84 1743290.6 0.6031695
SV0918 0.9813852 2.0134023 0.9757505 0.9941495 NaN 283671.12 5087150.9 0.6031695
SV0919 0.8197531 1.4795201 0.6622318 -0.3344454 NaN 127739.21 1545412.2 0.6031695
SV0920 0.8197531 1.1326478 0.7221003 0.2290888 NaN 96468.98 1696078.5 0.6031695
SV1001 0.7037774 1.3175936 0.7592891 -0.3572888 NaN 721160.90 12970963.1 0.8646296
SV1002 0.9125006 1.7205768 0.8478968 -0.0317179 44.97484 1789679.23 32208184.2 0.8646296
SV1003 1.0000000 2.3308096 1.0000000 0.8852041 57.05951 645320.82 11599952.0 0.8646296
SV1004 1.0000000 2.3387191 1.0000000 0.6981284 54.99719 603236.39 10831516.0 0.8646296
SV1019 0.7939786 1.7177703 0.7941934 0.1438960 45.14239 26306.72 350529.4 0.8646296
SV1005 0.5584869 1.0661489 0.6579609 -0.8638212 NaN 55849.55 995519.3 0.8646296
SV1006 0.6102633 1.1096382 0.6965141 -0.6509299 NaN 23791.31 311853.1 0.8646296
SV1007 0.7694303 1.6237757 0.7640597 0.0391638 44.72528 19103.39 330671.6 0.8646296
SV1099 0.4064082 0.7838809 0.6225419 -0.8132075 19.46150 721158.97 12970999.5 0.8646296
SV1008 0.9437146 1.9419446 0.9334826 0.2704000 48.59699 1789678.14 32208204.4 0.8646296
SV1009 0.6966340 1.2455084 0.7160466 -0.5515810 NaN 645318.60 11599992.3 0.8646296
SV1010 0.7146546 1.3574158 0.7307261 -0.9128488 NaN 603221.52 10831635.4 0.8646296
SV1011 0.6227347 1.1074493 0.7257810 -0.5441312 NaN 26303.49 350574.4 0.8646296
SV1012 1.0000000 2.2459372 1.0000000 0.6742230 NaN 55876.87 995441.1 0.8646296
SV1013 0.7940421 1.6656469 0.7685819 -0.2185450 NaN 23792.52 311836.9 0.8646296
SV1014 0.9051841 1.8479591 0.8370063 0.1075853 48.02604 19105.06 330641.8 0.8646296
SV1015 0.5967680 1.2989784 0.7004744 -0.3235243 NaN 721160.22 12970976.2 0.8646296
SV1016 0.6369158 1.2279354 0.7094783 -0.2529024 38.39021 1789677.59 32208214.6 0.8646296
SV1017 0.8044141 1.5875002 0.7351460 -0.0426384 44.30140 645319.59 11599974.2 0.8646296
SV1018 0.7345991 1.4221880 0.7195339 -0.4659205 NaN 603232.14 10831594.9 0.8646296
SV1101 0.6975749 1.2999396 0.6865261 -0.4423312 NaN 123088.77 1183243.4 0.3957102
SV1102 0.7832152 1.2982627 0.7725125 0.1541990 NaN 198489.75 3556781.7 0.3957102
SV1103 0.7881887 4.3937118 0.8346381 0.4074556 51.42843 114178.86 1071696.9 0.3957102
SV1104 0.6864198 1.3418635 0.6562222 -0.5680995 NaN 67600.41 1184351.3 0.3957102
SV1105 0.6864198 1.4690752 0.6461463 -0.6334632 NaN 8556414.80 153988455.8 0.3957102
SV1106 0.6864198 1.2178663 0.7312127 -0.0421299 NaN 21238990.80 382289739.8 0.3957102
SV1107 0.7538812 1.3856515 0.7343583 -0.1494876 NaN 7654576.51 137748867.9 0.3957102
SV1108 0.7761589 1.3925177 0.7687788 -0.4149154 NaN 7151763.80 128687156.1 0.3957102
SV1109 0.6864198 1.4743362 0.6401585 -0.7145474 NaN 123088.45 1183246.7 0.3957102
SV1110 0.7869416 2.6343177 0.8164785 0.2030698 64.31205 198519.75 3556692.2 0.3957102
SV1111 0.6864198 1.0854118 0.6482128 -0.4349362 NaN 114177.37 1071711.3 0.3957102
SV1112 0.7426199 NaN 0.7890106 -0.0154053 0.00000 67601.42 1184332.9 0.3957102
SV1113 0.6864198 1.0953235 0.7156986 -0.0757677 NaN 8556414.45 153988462.9 0.3957102
SV1201 0.6320988 0.8472507 0.6326526 -0.2960383 NaN 8065109.61 145160735.1 0.7133090
SV1202 0.8505454 1.2511691 0.8203728 -0.2261471 NaN 2906935.66 52300720.7 0.7133090
SV1203 0.6901312 1.1020180 0.6763247 -0.2238446 NaN 2716365.98 48853274.2 0.7133090
SV1204 0.7050162 1.1671677 0.6892715 -0.1690239 NaN 52170.66 518888.4 0.7133090
SV1205 0.7181056 1.1984668 0.6919186 0.0310242 NaN 88432.36 1576877.7 0.7133090
SV1299 0.6320988 0.7625390 0.6699700 -0.2296461 NaN 48187.41 466906.8 0.7133090
SV1298 0.6320988 8.8206145 0.5986130 -0.6249415 NaN 30446.57 519715.3 0.7133090
SV1206 0.6320988 1.2436113 0.6315013 -0.5259910 NaN 3249282.07 58469149.9 0.7133090
SV1207 0.8734902 1.6234447 0.8357310 -0.6999676 NaN 8065111.27 145160704.3 0.7133090
SV1208 0.6320988 0.7048255 0.6349923 -0.3411517 NaN 2906934.96 52300732.4 0.7133090
SV1209 0.7764533 1.8948880 0.7834278 0.2288746 55.13936 2716372.91 48853232.7 0.7133090
SV1210 0.9563663 1.4910862 0.9424479 -0.1168941 NaN 52170.84 518886.6 0.7133090
SV1211 0.6320988 0.7708205 0.6518670 -0.2325438 NaN 88439.55 1576868.1 0.7133090
SV1212 0.6786103 1.0269437 0.6676379 -0.1542049 NaN 48187.62 466904.6 0.7133090
SV1213 0.7353068 1.2654663 0.7123007 -0.4827705 NaN 30461.23 519637.1 0.7133090
SV1301 0.7110361 1.3701563 0.7019773 -0.3583585 NaN 4265080.42 76752735.3 0.6524864
SV1302 0.6361289 1.2052268 0.6566669 -0.3793201 NaN 10586624.58 190550153.7 0.6524864
SV1303 0.5889095 1.2903771 0.6457008 -0.6717706 NaN 3815642.83 68656615.7 0.6524864
SV1304 0.5456790 1.0160315 0.6053079 -0.7958604 NaN 3565159.33 64137141.0 0.6524864
SV1305 0.7357880 1.3056987 0.7221603 -0.4498431 NaN 73936.58 765691.7 0.6524864
SV1306 0.5937213 0.9788989 0.6530225 -0.4318472 NaN 129926.27 2325519.2 0.6524864
SV1307 0.7163359 1.3397625 0.7050064 -0.2226080 NaN 68123.37 686087.8 0.6524864
SV1308 0.7241744 1.3218159 0.7187034 -0.5524499 NaN 44276.06 774163.4 0.6524864
SV1309 0.9232063 2.5840442 0.9195016 0.5673967 73.79356 4265080.67 76752732.6 0.6524864
SV1310 0.7095852 1.5440643 0.7162713 -0.0007244 NaN 10586625.15 190550143.2 0.6524864
SV1311 0.7177671 1.3115432 0.7069371 -0.6396584 NaN 3815643.29 68656608.2 0.6524864
SV1312 0.5926358 1.1282502 0.6642266 -0.2625348 NaN 3565176.13 64137098.7 0.6524864
SV1313 0.6236773 1.1938280 0.6590482 -0.5704541 NaN 73936.27 765695.1 0.6524864
SV1314 0.5647067 0.8009826 0.6720331 -0.2939350 NaN 129938.75 2325492.5 0.6524864
SV1315 0.7443018 1.2325811 0.7201243 -0.7332861 NaN 68123.27 686088.6 0.6524864
SV1316 0.7705039 2.0847045 0.7950567 0.1482368 60.91138 44277.79 774132.0 0.6524864
SV1401 0.8580046 1.6137039 0.8081672 0.3384277 NaN 6058322.76 109013909.8 0.5450798
SV1402 0.8246914 1.4184870 0.6764192 -0.2023565 NaN 15037184.65 270654168.0 0.5450798
SV1403 0.8246914 -6.6297819 0.6728499 -0.0282550 NaN 5419884.12 97515429.1 0.5450798
SV1405 0.8444414 1.3111072 0.7873514 0.2868357 NaN 5063979.44 91098391.6 0.5450798
SV1406 0.8972752 3.1951770 0.8749481 0.7588240 65.87715 141181.62 1654665.0 0.5450798
SV1407 0.8246914 1.2247105 0.7615157 0.3808378 NaN 274316.15 4918562.1 0.5450798
SV1408 0.8248011 1.3588880 0.7612870 -0.0016599 NaN 129184.03 1469085.4 0.5450798
SV1409 0.8251224 1.1544322 0.7574377 0.2973474 NaN 93334.22 1639040.5 0.5450798
SV1410 0.8246914 1.2887059 0.6784833 -0.1698608 NaN 6058321.16 109013939.1 0.5450798
SV1411 0.8246914 1.5200657 0.7718324 0.3762832 NaN 15037185.49 270654152.5 0.5450798
SV1412 0.8246914 0.9667015 0.7265914 0.1274328 NaN 5419884.82 97515417.1 0.5450798
SV1413 0.8659087 1.5467050 0.8239596 0.6125127 NaN 5063988.04 91098359.4 0.5450798
SV1414 0.8748150 1.5844662 0.8461429 0.7214697 NaN 141180.86 1654674.5 0.5450798
SV1415 0.8246914 1.2768033 0.6976851 -0.0414692 NaN 274296.74 4918614.7 0.5450798
SV1416 0.8246914 0.8937032 0.7154690 0.1941221 NaN 129183.64 1469090.8 0.5450798
SV1417 0.8828154 1.4291488 0.8430895 0.7507730 NaN 93335.96 1639008.9 0.5450798
SV1418 0.8246914 0.9293655 0.6988305 0.1074128 NaN 6058321.37 109013935.7 0.5450798
SV1419 0.8684024 1.4608429 0.8228834 0.4506804 NaN 15037186.27 270654138.0 0.5450798
SV1420 1.0000000 NaN 1.0000000 0.7515436 0.00000 5419885.98 97515395.7 0.5450798
SV1421 0.8246914 0.9849501 0.7388802 0.2740771 NaN 5063978.50 91098397.2 0.5450798
SV1422 0.8246914 1.0829523 0.6957235 0.1035263 NaN 141179.09 1654696.1 0.5450798
SV1423 0.8593880 1.7157558 0.8248914 0.4276728 NaN 274303.71 4918585.2 0.5450798
SV0714 0.9135802 1.4345661 0.7926578 0.1688549 NaN 221865.59 2679622.6 0.3715732
SV1404 0.8246914 1.2659651 0.7482462 0.0728259 NaN 93335.04 1639025.6 0.5450798

As we can see some of the potential aggregation algorithm are not providing results from some location. We will therefore exclude them from the rest of the analysis.

Differences between algorithms

The various Index can be normalised again on a 0 to 1 scale in order to be compared. A specific treatment if necessary for index based on Factor analysis

Location Ranking with different algorithms
Benef_Doubt Benef_Doubt_Dir Factor Mean_Min Mazziotta_Pareto Wroclaw
SV0101 180.0 186 158 56 36 148.0
SV0102 84.0 60 183 27 38 146.0
SV0103 147.0 158 212 197 196 149.0
SV0104 110.0 113 196 252 251 144.0
SV0105 231.0 231 266 193 193 150.0
SV0106 64.5 47 149 189 189 145.0
SV0107 56.5 17 110 63 40 142.5
SV0108 87.0 80 117 85 94 142.5
SV0109 60.5 22 156 55 37 141.0
SV0110 60.5 79 162 26 39 147.0
SV0111 67.0 28 102 196 197 139.0
SV0112 60.5 40 105 251 252 140.0
SV0201 190.0 147 219 222 223 200.0
SV0209 190.0 157 198 216 216 202.0
SV0202 231.0 231 264 114 100 206.0
SV0203 231.0 231 210 130 130 205.0
SV0204 188.0 89 191 107 95 198.0
SV0205 190.0 150 223 94 101 203.0
SV0206 195.0 195 166 236 236 204.0
SV0207 192.5 159 217 259 259 201.0
SV0208 192.5 121 189 223 222 199.0
SV0301 231.0 231 211 217 221 208.0
SV0302 231.0 231 218 125 127 207.0
SV0303 231.0 231 240 142 141 219.0
SV0307 231.0 231 249 121 116 229.0
SV0304 231.0 231 228 115 122 212.0
SV0305 231.0 231 261 243 242 233.0
SV0306 231.0 231 256 263 260 228.0
SV0308 231.0 231 241 225 226 224.0
SV0309 231.0 231 247 220 218 226.0
SV0310 231.0 231 263 129 123 239.0
SV0399 231.0 231 239 141 142 231.0
SV0311 231.0 231 243 119 118 222.0
SV0312 231.0 231 237 117 120 221.0
SV0313 231.0 231 214 244 241 216.0
SV0314 231.0 231 231 260 263 214.0
SV0315 231.0 231 236 224 227 223.0
SV0316 231.0 231 205 219 219 220.0
SV0317 231.0 231 222 127 125 210.0
SV0318 231.0 231 234 140 143 218.0
SV0319 231.0 231 251 122 115 235.0
SV0320 231.0 231 246 116 121 227.0
SV0321 231.0 231 260 242 243 237.0
SV0322 231.0 231 230 261 262 211.0
SV0323 231.0 231 244 226 225 234.0
SV0324 231.0 231 209 218 220 209.0
SV0325 231.0 231 265 128 124 232.0
SV0326 231.0 231 225 143 140 213.0
SV0327 231.0 231 258 120 117 230.0
SV0328 231.0 231 252 118 119 225.0
SV0329 231.0 231 255 241 244 240.0
SV0330 231.0 231 245 262 261 215.0
SV0331 231.0 231 254 227 224 236.0
SV0332 231.0 231 253 221 217 238.0
SV0333 231.0 231 232 126 126 217.0
SV0401 91.0 163 160 96 102 163.0
SV0402 183.0 190 213 65 43 165.0
SV0403 83.0 153 134 37 49 162.0
SV0404 66.0 144 148 208 209 160.0
SV0405 18.5 74 104 253 254 155.0
SV0406 18.5 90 103 199 200 157.0
SV0407 231.0 231 220 195 194 166.0
SV0408 18.5 62 81 69 47 154.0
SV0409 38.0 52 61 95 103 153.0
SV0410 51.0 65 87 64 44 156.0
SV0411 81.0 156 143 36 50 161.0
SV0412 104.0 169 204 209 208 158.0
SV0413 50.0 127 137 254 253 159.0
SV0414 118.0 182 184 200 199 164.0
SV0415 54.0 81 9 194 195 152.0
SV0416 18.5 9 30 68 48 151.0
SV0501 185.0 188 238 46 55 42.0
SV0502 32.0 45 85 16 8 28.0
SV0503 99.0 93 150 9 10 38.5
SV0504 115.0 143 175 153 153 37.0
SV0505 3.0 5 4 176 176 21.0
SV0506 35.0 37 67 146 148 31.0
SV0507 39.0 55 101 145 144 29.5
SV0508 27.0 23 33 18 14 25.0
SV0509 97.0 116 76 44 57 34.0
SV0510 231.0 231 165 17 7 36.0
SV0511 82.0 125 140 8 11 40.0
SV0512 117.0 148 89 154 152 33.0
SV0513 116.0 145 80 177 175 35.0
SV0514 100.0 126 190 148 146 38.5
SV0515 5.0 19 74 144 145 29.5
SV0516 14.0 10 44 19 13 26.0
SV0517 52.0 50 48 45 56 32.0
SV0518 2.0 3 19 15 9 22.0
SV0519 33.0 43 45 7 12 27.0
SV0520 23.0 24 28 152 154 23.0
SV0521 6.0 14 47 175 177 24.0
SV0522 98.0 123 187 147 147 41.0
SV0601 111.0 167 199 170 168 115.0
SV0602 69.0 91 135 34 28 108.0
SV0603 11.0 6 31 51 70 99.0
SV0604 11.0 16 39 31 18 100.0
SV0605 86.0 100 141 12 20 112.0
SV0606 46.0 75 142 183 185 102.0
SV0607 11.0 1 17 234 235 94.0
SV0608 11.0 13 56 173 174 97.0
SV0609 11.0 7 42 168 170 98.0
SV0610 34.0 30 60 33 29 101.0
SV0611 93.0 142 152 52 69 114.0
SV0612 31.0 32 59 30 19 96.0
SV0613 15.0 21 66 11 21 103.5
SV0614 92.0 110 95 184 184 110.0
SV0615 21.0 58 121 235 234 107.0
SV0616 47.0 41 99 174 173 109.0
SV0617 28.0 49 107 169 169 103.5
SV0618 103.0 140 208 35 27 113.0
SV0619 30.0 54 123 50 71 111.0
SV0620 70.0 68 54 32 17 105.0
SV0621 16.0 20 58 10 22 95.0
SV0622 148.0 166 36 185 183 106.0
SV0701 170.0 133 203 258 256 192.0
SV0702 170.0 106 192 204 205 181.0
SV0703 170.0 70 173 201 202 180.0
SV0704 177.5 179 197 106 98 191.0
SV0705 163.0 162 201 123 129 187.0
SV0706 179.0 99 177 99 93 183.0
SV0707 170.0 160 224 89 97 194.0
SV0708 170.0 170 227 213 212 195.0
SV0709 177.5 77 178 257 257 185.0
SV0710 194.0 194 259 205 204 197.0
SV0711 170.0 122 185 202 201 193.0
SV0712 170.0 141 181 105 99 184.0
SV0713 170.0 115 172 124 128 186.0
SV0715 170.0 161 221 100 92 189.0
SV0716 170.0 175 229 90 96 196.0
SV0717 170.0 134 215 212 213 190.0
SV0718 170.0 109 195 256 258 182.0
SV0801 231.0 231 120 239 238 251.0
SV0802 231.0 231 82 231 232 254.0
SV0803 231.0 231 112 92 67 257.0
SV0804 231.0 231 147 102 108 262.0
SV0805 231.0 231 119 86 63 252.0
SV0806 231.0 231 146 49 58 263.0
SV0807 231.0 231 96 248 250 245.0
SV0808 231.0 231 131 266 264 248.0
SV0809 231.0 231 94 238 239 247.0
SV0810 231.0 231 118 230 233 250.0
SV0811 231.0 231 114 91 68 243.0
SV0812 231.0 231 153 103 107 265.0
SV0813 231.0 231 138 87 62 249.0
SV0814 231.0 231 113 48 59 253.0
SV0815 231.0 231 193 250 248 258.0
SV0816 231.0 231 78 265 265 241.0
SV0817 231.0 231 133 240 237 259.0
SV0818 231.0 231 100 233 231 255.0
SV0819 231.0 231 161 93 66 266.0
SV0820 231.0 231 169 104 106 264.0
SV0821 231.0 231 139 88 61 261.0
SV0822 231.0 231 124 47 60 246.0
SV0823 231.0 231 116 249 249 260.0
SV0824 231.0 231 75 264 266 242.0
SV0825 231.0 231 64 237 240 244.0
SV0826 231.0 231 186 232 230 256.0
SV0901 119.5 114 200 80 87 84.0
SV0902 126.0 67 170 111 114 79.0
SV0903 126.0 94 180 74 72 85.0
SV0904 126.0 83 176 61 84 86.0
SV0905 132.0 137 154 182 181 82.5
SV0906 126.0 87 188 229 228 82.5
SV0907 157.0 172 226 171 172 88.0
SV0908 126.0 82 179 165 164 81.0
SV0909 126.0 120 207 81 86 89.0
SV0910 231.0 231 262 113 112 93.0
SV0911 126.0 35 145 72 74 76.0
SV0912 182.0 187 248 62 83 92.0
SV0913 126.0 64 157 181 182 77.0
SV0914 126.0 33 126 228 229 75.0
SV0915 161.0 185 235 172 171 90.0
SV0916 126.0 85 167 164 165 80.0
SV0917 231.0 231 168 79 88 87.0
SV0918 187.0 193 257 112 113 91.0
SV0919 126.0 42 98 73 73 74.0
SV0920 119.5 104 194 60 85 78.0
SV1001 73.0 129 88 139 137 6.0
SV1002 162.0 183 151 151 149 15.0
SV1003 231.0 231 250 136 134 20.0
SV1004 231.0 231 242 133 131 19.0
SV1019 112.0 154 182 6 5 13.5
SV1005 7.0 38 8 28 42 2.0
SV1006 29.0 71 35 3 2 4.0
SV1007 101.0 132 164 1 4 13.5
SV1099 1.0 8 14 137 139 5.0
SV1008 184.0 191 202 150 150 18.0
SV1009 71.0 97 52 134 136 7.0
SV1010 77.0 111 4 131 133 1.0
SV1011 36.0 107 34 5 6 3.0
SV1012 231.0 231 233 29 41 12.0
SV1013 113.0 135 97 4 1 10.0
SV1014 160.0 178 155 2 3 17.0
SV1015 26.0 76 73 138 138 9.0
SV1016 49.0 88 84 149 151 11.0
SV1017 114.0 118 129 135 135 16.0
SV1018 88.0 102 46 132 132 8.0
SV1101 72.0 61 50 71 51 168.0
SV1102 107.0 139 163 97 105 173.0
SV1103 109.0 176 206 67 45 177.0
SV1104 60.5 34 32 38 54 169.0
SV1105 60.5 26 21 211 210 171.0
SV1106 60.5 112 130 255 255 176.0
SV1107 96.0 117 109 203 203 174.0
SV1108 105.0 136 53 198 198 172.0
SV1109 55.0 18 11 70 52 167.0
SV1110 108.0 165 171 98 104 179.0
SV1111 64.5 27 51 66 46 170.0
SV1112 94.0 151 136 39 53 178.0
SV1113 56.5 96 122 210 211 175.0
SV1201 41.0 12 77 206 207 50.0
SV1202 150.0 168 92 158 157 56.0
SV1203 68.0 56 93 155 156 51.0
SV1204 74.0 63 106 24 24 47.0
SV1205 80.0 66 144 53 76 55.0
SV1299 41.0 48 91 22 16 52.0
SV1298 43.5 2 26 13 26 44.0
SV1206 41.0 11 37 159 159 43.0
SV1207 155.0 177 13 207 206 45.0
SV1208 43.5 15 69 157 158 46.0
SV1209 106.0 146 174 156 155 57.0
SV1210 186.0 192 115 25 23 54.0
SV1211 45.0 29 90 54 75 49.0
SV1212 53.0 46 108 23 15 53.0
SV1213 89.0 92 43 14 25 48.0
SV1301 76.0 78 63 166 167 68.0
SV1302 48.0 36 55 214 215 69.0
SV1303 22.0 25 18 162 163 58.0
SV1304 4.0 4 4 160 161 59.0
SV1305 90.0 105 40 43 32 66.0
SV1306 25.0 31 41 77 90 63.0
SV1307 78.0 84 79 41 30 70.0
SV1308 85.0 101 27 20 35 60.0
SV1309 181.0 189 216 167 166 73.0
SV1310 75.0 98 125 215 214 71.0
SV1311 79.0 86 12 163 162 61.0
SV1312 24.0 44 72 161 160 64.0
SV1313 37.0 39 22 42 33 62.0
SV1314 8.0 51 65 78 89 67.0
SV1315 95.0 103 4 40 31 65.0
SV1316 102.0 155 49 21 34 72.0
SV1401 151.0 164 83 192 190 129.0
SV1402 138.0 57 4 245 247 120.0
SV1403 138.0 53 20 186 188 116.0
SV1405 149.0 149 70 179 179 126.0
SV1406 159.0 184 159 84 80 137.0
SV1407 138.0 131 86 110 109 132.0
SV1408 145.0 130 23 76 64 121.0
SV1409 146.0 128 71 57 79 125.0
SV1410 144.0 59 4 190 192 117.0
SV1411 138.0 138 57 246 246 130.0
SV1412 138.0 108 25 187 187 128.0
SV1413 153.0 173 111 180 178 135.0
SV1414 156.0 181 132 83 81 136.0
SV1415 138.0 72 4 108 111 118.0
SV1416 138.0 95 29 75 65 122.5
SV1417 158.0 180 127 59 77 134.0
SV1418 138.0 73 16 191 191 122.5
SV1419 154.0 171 68 247 245 133.0
SV1420 231.0 231 128 188 186 138.0
SV1421 138.0 119 38 178 180 127.0
SV1422 138.0 69 15 82 82 119.0
SV1423 152.0 174 62 109 110 131.0
SV0714 170.0 152 24 101 91 188.0
SV1404 138.0 124 10 58 78 124.0

Let’s write this back to the excel doc

We can now build a visualization for the comparison between different valid methods.

Index sensitivity to method

As we can see, the final ranking for each location is very sensitive to the methods.

An approach to select the method can be to identify, average ranks per method in order to identify the method that is getting closer this average ranks.

we can first visualise those average ranks.

Next is to compute standard deviation for each method.

#> 
#> [0.406,0.634) [0.634,0.736) [0.736,0.825) [0.825,0.914) [0.914,  Inf] 
#>            45            44            55            34            88

Severity Index on a map

We can now visualize the thematic indicator on a map.

An initial visualisation will be to present both the severity and the population size affected by this severity.

Annex

Leveraging Facebook data for Good dataset

People who use Facebook on a mobile device have the option of providing their precise location in order to enable products like Nearby Friends and Find Wi-Fi and to get local content and ads. Different type of products are produced by the Facebok Data for Good Team by aggregating and de-identifying this data. Only people who opt in to Location History and background location collection are included. People with very few location pings in a day are not informative for these trends, and, therefore, we include only those people whose location is observed for a meaningful period of the day.

Central America indeed has an important [number of facebook users]and Facebook data for good has released a few dataset

How to re-use that script

Get the correct project name in HDX